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DOE OSTI2024

Data and Code for Atomic Scale Etching of Diamond: Insights from Molecular Dynamics Simulations

This work investigates the effects of argon ions, hydrogen atoms, and hydrogen ions on the diamond (100) surface using classical molecular dynamics simulations. The purpose of this investigation was to asses plasma processing techniques for applications in quantum device manufacturing. The simulations suggest that combining argon ion smoothing with selective, near threshold energy H removal of amorphous C could be an effective strategy for diamond surface engineering, leading to more reliable and sensitive diamond color center devices. Results were found to differ significantly with interatomic potential, and an analysis of these differences was also carried out. Included in this repository are LAMMPS source files, input scripts, and plotting scripts required to reproduce the data. Also included are the output data required to make all the plots included in the associated publication.

Brenner↗

DOE OSTI2026

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential

A molecular-level understanding of electrolyte solvation structure and ion–ion correlations is critical to developing next-generation battery chemistries. Atomistic simulation capabilities with sufficient accuracy, speed, and transferability to deliver reliable structural insights while avoiding arduous system-specific reparameterization are thus highly desirable. Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse data sets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000× faster than DFT. While previous MLIP training data sets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) data set provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pretrained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation–solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations beyond the limits of classical force fields and direct DFT molecular dynamics, serving as a powerful tool for accelerating the design of next-generation Na-ion battery electrolytes and beyond.

MLIPs↗

DOE OSTI2020

Validating first-principles molecular dynamics calculations of oxide/water interfaces with x-ray reflectivity data

Metal oxide/water interfaces play a crucial role in many electrochemical and photocatalytic processes, such as photoelectrochemical water splitting, the creation of fuel from sunlight, and electrochemical CO 2 reduction. First-principles electronic structure calculations can reveal unique insights into these processes, such as the role of the alignment of the oxide electronic energy levels with those of liquid water. An essential prerequisite for the success of such calculations is the ability to predict accurate structural models of these interfaces, which in turn requires careful experimental validation. Here we report a general, quantitative validation protocol for first-principles molecular dynamics simulations of oxide/aqueous interfaces. The approach makes direct comparisons of interfacial x-ray reflectivity (XR) signals from experimental measurements and those obtained from ab initio simulations with semilocal and van der Waals functionals. The protocol is demonstrated here for the case of the Al 2 O 3 (001)/water interface, one of the simplest oxide/water interfaces. We discuss the technical requirements needed for validation, including the choice of the density functional, the simulation cell size, and the optimal choice of the thermodynamic ensemble. Our results establish a general paradigm for the validation of structural models and interactions at solid/water interfaces derived from first-principles simulations. Furthermore, while there is qualitative agreement between the simulated structures and the experimental best-fit structure, direct comparisons of simulated and measured XR intensities show quantitative discrepancies that derive from both bulk regions (i.e., alumina and water) as well as the interfacial region, highlighting the need for accurate density functionals to properly describe interfacial interactions. Our results show that XR data are sensitive not only to the atomic structure (i.e., the atom locations) but also to the electron-density distributions in both the substrate and at the interface.

36 MATERIALS SCIENCE↗

DOE OSTI2022

An idea to explore: How an interdisciplinary undergraduate course exploring a global health challenge in molecular detail enabled science communication and collaboration in diverse audiences

Abstract Communication and collaboration are key science competencies that support sharing of scientific knowledge with experts and non‐experts alike. On the one hand, they facilitate interdisciplinary conversations between students, educators, and researchers, while on the other they improve public awareness, enable informed choices, and impact policy decisions. Herein, we describe an interdisciplinary undergraduate course focused on using data from various bioinformatics data resources to explore the molecular underpinnings of diabetes mellitus (Types 1 and 2) and introducing students to science communication. Building on course materials and original student‐generated artifacts, a series of collaborative activities engaged students, educators, researchers, healthcare professionals and community members in exploring, learning about, and discussing the molecular bases of diabetes. These collaborations generated novel educational materials and approaches to learning and presenting complex ideas about major global health challenges in formats accessible to diverse audiences.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRS1986

Clump 1 - An unusual molecular cloud complex near the Galactic center

The properties of the (C-12)O and (C-13)O emission from Clump I, a complex of molecular clouds which shows the largest noncircular velocities of any molecular material known in the Milky Way, are studied. Observations of the J = 1-0 rotational transitions of the above molecules are reported, and the large-scale distribution and kinematics of the molecular gas in the direction of Clump I are discussed. The small-scale structure is described based on one arcmin resolution of (C-13)O data. Physical properties of the molecular material are derived, and the dynamics of the Clump I clouds are discussed in the context of their stability against Galactic tidal forces. It is concluded that all the clouds in the complex are gravitationally bound, although their outer envelopes may be tidally disrupted by the Galactic potential. The relative motion of the clouds is qualitatively similar to giant molecular cloud complexes in spiral arms of the outer Galactic disk, but the velocities are higher.

Bania, T. M.↗

NASA NTRS1998

Modeling Molecular Opacities Using Galileo Probe Observations

Data from the Net Flux Radiometer (NFR) experiment on the Galileo mission has been successfully collected and analyzed through 1996. As data was received, the best available laboratory spectroscopic data and was used in the reduction procedure. Since then newer and more accurate spectroscopic data has become available. During this agreement, research efforts focused on re-analysis of certain aspects of the data which benefited from recent improvements in spectroscopic databases, and from a more exact treatment of certain aspects of the physical theory of line shape and line broadening. In addition, a brief survey was completed of the available information, both theoretical and observational, of the absorption, reflection and transmission spectra of the atmospheres of the various types of planets that could reasonably be expected to occur in other solar systems.

Freedman, Richard S.↗

NASA NTRS1998

Modeling Molecular Opacities using Galileo Probe Observations

Freedman, Richard S.↗

DOE OSTI2024

Cellulose molecular weight of corn stover with cotreatment

Here we present a dataset of cellulose molecular weights from corn stover after a cotreatment process. The corn stover sample was subjected to a combination of mechanical disruption (ball milling) and consolidated bioprocessing (CBP) process using the bacterium C. thermocellum at 60 grams/L solids loadings. Cellulose was isolated from control, no cotreatment control, and cotreatment corn stover. Cellulose was then derivatized using anhydrous pyridine and phenyl isocyanate over 48 h at 70 ºC. The reaction was quenched by anhydrous methanol. Methanol and water mixture (7:3, v/v) was added dropwise to promote precipitation of the cellulose derivative. The solids were collected by filtration and thoroughly washed with the methanol and water mixture followed by water. The cellulose derivative was dried overnight under vacuum at 40°C. The obtained cellulose tricarbanilates was then dissolved in tetrahydrofuran (THF) at 1.0 mg/mL and the solution was filtered through a 0.45 µm PTFE filter and placed in an auto-sampler vial. The molecular weight of cellulose was measured by Agilent GPC SECurity 1200 system equipped with four Waters Styragel columns (i.e., HR0.5, HR2, HR4, and HR6) and a UV detector (270 nm). THF was used as the mobile phase. Data collection and processing was performed by Polymer Standards Service WinGPC Unity software (Build 6807). The molecular weight was calculated relative to the polystyrene calibration curve. The data on molecular weights provided information about the effects of cotreatment on corn stover cellulose molecular changes.

Cellulose, molecular weights, corn stover, cotreat↗

NASA NTRS1986

Spectral studies related to dissociation of HBr, HCl and BrO

Concern over halogen catalyzed decomposition of O3 in the upper atmosphere has generated need for data on the atomic and molecular species X, HX and XO (where X is Cl and Br). Of special importance are Cl produced from freon decomposition and Cl and Br produced from natural processes and from other industrial and agricultural chemicals. Basic spectral data is provided on HCl, HBr, and BrO necessary to detect specific states and energy levels, to enable detailed modeling of the processes involving molecular dissociation, ionization, etc., and to help evaluate field experiments to check the validity of model calculations for these species in the upper atmosphere. Results contained in four published papers and two major spectral compilations are summarized together with other results obtained.

Ginter, M. L.↗

DOE OSTI2020

Milestone 2.7: Evaluation of Techniques for the Measurement of Molecular Hydrogen Gas in Helium Matrices

Published data by Task 2 (Oxyhydroxide Layer Radiolytic Gas Generation Resolution) for Technical Considerations and Challenges for Extended (>50 yrs) Dry Storage of Aluminum Cladded Spent Nuclear Fuel (ASNF), demonstrated that radiolytic molecular hydrogen (H2) production from gamma irradiated aluminum alloy 1100 (Al-1100) coupons exhibited significant dependence on backfill gaseous environment conditions: air, due to the presence of oxygen, completely inhibited H2 production; nitrogen promoted H2 production; and argon yielded more H2 than nitrogen environments. The concern here is that helium has been proposed as the backfill gas for extended storage and is more inert than argon, which may translate into significantly more H2 production than current Task 3 argon-based models predict. However, the measurement of H2 in helium media was not possible using the previously establish gas chromatography (GC) flame ionization detector, due to similarities in thermal conductivity. This milestone was initiated to evaluate an alternative GC approach that employed a mercuric oxide (HgO) reduction gas detector (RGD). Using the HgO RGD setup, H2 was successfully calibrated in the presence of a helium carrier gas, and then subsequently measured in control and irradiated crush-tube sample vials, consistent with previous sample measurements. Overall, the HgO RGD approach was found to be sufficient for future helium environment irradiations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2024

The Advantages of Combining Morphological and Molecular Methods to Characterise Zooplankton Communities: A Case Study of the UNESCO Biosphere Reserve of the Berlengas Archipelago, Portugal

Understanding the ecological dynamics of zooplankton communities is crucial to precisely assessing the health of marine ecosystems and their management. Metabarcoding has contributed to a better understanding of biodiversity in marine environments. However, this methodology still requires protocol optimisation. Here, we used a complementary approach combining molecular and morphological identification methods to identify the zooplankton community inhabiting the Berlengas Archipelago, Portugal. The presence of non-indigenous species was also assessed. The results showed that the metabarcoding approach outperformed the classical morphological identification method, detecting more species with higher resolution. Nevertheless, the classical method was able to identify species that were not detected by the molecular approaches, probably due to a lack of reference data in the databases. The comparison between different molecular approaches showed that COI and bulk DNA gave better results than 18S rRNA and eDNA by detecting higher species diversity. However, complementarities were observed between them. Molecular tools also proved effective in identifying several potential non-indigenous species, identifying, for the first time, several potentially unreported NIS inhabiting the Portuguese marine ecosystems. Overall, our results confirmed the importance of combining both classical and molecular methods to obtain a more refined assessment of the zooplanktonic communities in marine environments.

Simões, Marco (ORCID:0000000179957304)↗

NASA NTRS2007

Huygens GCMS Results from Titan

The Huygens Probe executed a successful entry, descent and impact on the Saturnian moon of Titan on January 14, 2005. The Gas Chromatograph Mass Spectrometer (GCMS) instrument conducted isotopic and compositional measurements throughout the two and one half hour descent from 146 km altitude, and on the surface for 69 minutes until loss of signal from the orbiting Cassini spacecraft. The GCMS incorporated a quadrupole mass filter with a secondary electron multiplier detection system. The gas sampling system provided continuous direct atmospheric composition measurements and batch sampling through three gas chromatographic (GC) columns, a chemical scrubber and a hydrocarbon enrichment cell. The GCMS gas inlet was heated to prevent condensation, and to evaporate volatiles from the surface after impact. Data products from the GCMS included altitude profiles of the major atmospheric constituents dinitrogen (N2) and methane (CH4), isotope ratios of 14N/15N, 12C/13C, and D/H, mole fractions of radiogenic argon (40Ar) and primordial argon (36Ar), and upper limits on the mole fractions of neon, krypton and xenon, which were found to be absent. Surface measurements confirmed the presence of ethane (C2H6) and cyanogen (C2N2). Later data products expanded atmospheric profiles to include the surface response of C2N2. C2H6, acetylene (C2H2), and carbon dioxide (CO2). More recent results include the profiles of benzene (C6H6) and molecular hydrogen (H2). The GCMS data are being further analyzed to obtain higher precision results and to identify other trace species ion the atmosphere and evaporating from the surface.

Niemann, Hasso B.↗

NASA NTRS1986

CO mapping of the Orion molecular cloud: The influence of star formation on cloud structure

Regions of massive star formation have long been believed to have a profound influence on the structure of their surrounding molecular clouds. The ways in which massive star formation has altered the structure and kinematics of the Orion Molecular Cloud are discussed. The data to be discussed consists of a large scale map of the CO J=1-0 emission from approximately 3 square degrees of OMC-1. During 1985, the Five College Radio Astronomy Observatory 14M antenna was used to map a 2 deg x 1 deg region centered on alpha(1950) = 5(h)33(m)00(s) delta(1950) = -5 deg 30 min. The region mapped in 1985 covers the well known HII regions M42, M43, and NGC1977, and the CO map contains abundant evidence of the interaction between these regions and the molecular cloud. Indeed, the global structure of the cloud appears to have been strongly influenced by the continuous formation of massive stars within the cloud. Individual instances of some of these features are discussed. There appear to be two classes of features which are indicative of this interaction: CO bright rims and CO holes. During 1986, we have undertaken further mapping of OMC-1 to the south of the region covered by the 1985 map. This portion of the cloud contains significant regions of star formation, but O star formation has not occured and large HII regions have not developed to alter the appearance of the cloud. A detailed map of this region is thus an opportunity to view the structure of the molecular cloud before it has been altered by massive star formation. Preliminary analysis of data obtained in this region suggests that the structure and kinematics of the southern portion of the Orion cloud are indeed dramatically different from those of the region previously mapped. Comparison of the two regions thus supports models of the development of structure in molecular clouds through interaction with the HII regions formed within them.

Schloerb, F. P.↗

DOE OSTI2021

Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE

Imaging N-glycan spatial distribution in tissues using mass spectrometry imaging (MSI) is emerging as a promising tool in biological and clinical applications. However, there is currently no high throughput tool for visualization and molecular annotation of N-glycans in MSI data, which significantly slows down data processing and hampers the applicability of this approach. In this work, we present how METASPACE, an open-source cloud engine for molecular annotation of MSI data, can be used to automatically annotate, visualize, analyze, and interpret high-resolution mass spectrometry-based spatial N-glycomics data. METASPACE is an emerging tool in spatial metabolomics, but the lack of compatible glycan databases has limited its application for comprehensive N-glycan annotations from MSI datasets. We created NGlycDB, a public database of N-glycans, by adapting available glycan databases. We demonstrate the applicability of NGlycDB in METASPACE by analyzing MALDI-MSI data from formalin-fixed paraffin-embedded (FFPE) human kidney and mouse lung tissue sections. We added NGlycDB to METASPACE for public use, thus facilitating applications of MSI in glycobiology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Exploring Li-Ion Transport Properties of Li 3 TiCl 6 : A Machine Learning Molecular Dynamics Study

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li 3 TiCl 6 cathode at six different temperatures, ranging from 25°C to 100°C. In this work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to describe the Li-ion transport mechanism in Li 3 TiCl 6 . Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li 3 TiCl 6 cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.

Selvaraj, Selva Chandrasekaran (ORCID:000000029023↗

DOE OSTI2021

Computation of the Thermal Expansion Coefficient of Graphene with Gaussian Approximation Potentials

Direct experimental measurement of thermal expansion coefficient without substrate effects is a challenging task for two-dimensional (2D) materials, and its accurate estimation with large-scale ab initio molecular dynamics is computationally very expensive. Machine learning-based interatomic potentials trained with ab initio data have been successfully used in molecular dynamics simulations to decrease the computational cost without compromising the accuracy. In this work, we investigated using Gaussian approximation potentials to reproduce the density functional theory-level accuracy for graphene within both lattice dynamical and molecular dynamical methods, and to extend their applicability to larger length and time scales. Two such potentials are considered, GAP17 and GAP20. GAP17, which was trained with pristine graphene structures, is found to give closer results to density functional theory calculations at different scales. Further vibrational and structural analyses verify that the same conclusions can be deduced with density functional theory level in terms of the reasoning of the thermal expansion behavior, and the negative thermal expansion behavior is associated with long-range out-of-plane phonon vibrations. Thus, it is argued that the enabled larger system sizes by machine learning potentials may even enhance the accuracy compared to small-size-limited ab initio molecular dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗